Overview
- Describes a new reliable forecasting technique that works by learning the evolution-driving function
- Presents a way of comparing two disparately-long time series datasets via a distance between graphs
- Introduces a new learning technique that permits generation of absent training data, with applications
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About this book
This book introduces the concept of “bespoke learning”, a new mechanistic approach that makes it possible to generate values of an output variable at each designated value of an associated input variable. Here the output variable generally provides information about the system’s behaviour/structure, and the aim is to learn the input-output relationship, even though little to no information on the output is available, as in multiple real-world problems. Once the output values have been bespoke-learnt, the originally-absent training set of input-output pairs becomes available, so that (supervised) learning of the sought inter-variable relation is then possible. Three ways of undertaking such bespoke learning are offered: by tapping into system dynamics in generic dynamical systems, to learn the function that causes the system’s evolution; by comparing realisations of a random graph variable, given multivariate time series datasets of disparate temporal coverage; and by designing maximally information-availing likelihoods in static systems. These methodologies are applied to four different real-world problems: forecasting daily COVID-19 infection numbers; learning the gravitational mass density in a real galaxy; learning a sub-surface material density function; and predicting the risk of onset of a disease following bone marrow transplants. Primarily aimed at graduate and postgraduate students studying a field which includes facets of statistical learning, the book will also benefit experts working in a wide range of applications. The prerequisites are undergraduate level probability and stochastic processes, and preliminary ideas on Bayesian statistics.
Keywords
Table of contents (5 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Learning in the Absence of Training Data
Authors: Dalia Chakrabarty
DOI: https://doi.org/10.1007/978-3-031-31011-9
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-31010-2Published: 14 July 2023
Softcover ISBN: 978-3-031-31013-3Published: 15 July 2024
eBook ISBN: 978-3-031-31011-9Published: 13 July 2023
Edition Number: 1
Number of Pages: XVIII, 227
Number of Illustrations: 13 b/w illustrations, 16 illustrations in colour
Topics: Statistical Theory and Methods, Bayesian Inference, Data Mining and Knowledge Discovery, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Probability Theory and Stochastic Processes